Caring for Data’s Soul

Curation matters for data quality! Hardly any survey data user would disagree with this statement. But how much of a difference it makes is difficult to count. In this paper, we will illustrate on the example of data from two cross-national social survey programs, the European Value Study (EVS) and the International Social Survey Programme (ISSP), the most common errors that occur in uncurated international comparative data and draw attention to the problems that can arise from such errors in analyses’ results. To facilitate quality assessment and enable the assessment of data quality variation between countries within a survey, we developed a scheme that categorizes these errors, helps quantify them, and assigns them to possible curation measures. Based on this scheme, we developed an indicator that is called the Curation Impact Factor (CIF) that puts a concrete number on the data quality improvement due to curation effort and allows for comparability even across surveys. Therefore, the CIF could potentially be used to justify the use of resources for data curation in any survey data life cycle (e.g., in grant applications).